Saif Rayyan
Impact in
-
- Online Learning and Analytics
- Nuclear and High Energy Physics top 10%
- Black Holes and Theoretical Physics
- Particle physics theoretical and experimental studies
- Neutrino Physics Research
Papers in
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- Online and Blended Learning 6
- Innovations in Educational Methods 3
- Science Education and Pedagogy 2
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- Online Learning and Analytics 6
- Co-authors
- Tatsu Takeuchi (3 shared papers)Naotoshi Okamura (3 shared papers)Djordje Minić (1 shared paper)Lay Nam Chang (1 shared paper)Sándor Benczik (1 shared paper)David E. Pritchard (9 shared papers)Daniel Seaton (8 shared papers)Yoav Bergner (3 shared papers)
- Journals
- Journal of Computer Assisted Learning (1 paper)Applied Optics (1 paper)Journal of Computing in Higher Education (1 paper)American Journal of Physics (1 paper)AIP conference proceedings (5 papers)
- Partner nations
- United StatesChileSouth Korea
In The Last Decade
Saif Rayyan
16 papers receiving 442 citations
Peers
Comparison fields: 5 of 53
- Computer Science Applications 166
- Nuclear and High Energy Physics 204
- Statistical and Nonlinear Physics 173
- Astronomy and Astrophysics 97
- Education 99
Countries citing papers authored by Saif Rayyan
This map shows the geographic impact of Saif Rayyan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Saif Rayyan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saif Rayyan more than expected).
Fields of papers citing papers by Saif Rayyan
This network shows the impact of papers produced by Saif Rayyan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Saif Rayyan. The network helps show where Saif Rayyan may publish in the future.
Co-authors
The 25 scholars most cited alongside Saif Rayyan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2002 | 173 | |
| 2 | Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory | 2012 | 66 |
| 3 | 2017 | 64 | |
| 4 | 2004 | 36 | |
| 5 | 2016 | 30 | |
| 6 | 2003 | 19 | |
| 7 | 2014 | 14 | |
| 8 | 2014 | 10 | |
| 9 | 2012 | 9 | |
| 10 | 2012 | 9 | |
| 11 | 2014 | 7 | |
| 12 | 2010 | 6 | |
| 13 | 2012 | 5 | |
| 14 | 2010 | 5 | |
| 15 | 2023 | 2 | |
| 16 | 2023 | 1 | |
| 17 | 2016 | 1 |
About Saif Rayyan
Saif Rayyan is a scholar working on Education, Computer Science Applications, Developmental and Educational Psychology, Media Technology and Nuclear and High Energy Physics, having authored 17 papers that have together received 457 indexed citations. Recurring topics across this work include Online and Blended Learning (6 papers), Online Learning and Analytics (6 papers), Innovative Teaching and Learning Methods (4 papers), Innovations in Educational Methods (3 papers), Experimental Learning in Engineering (3 papers), Dark Matter and Cosmic Phenomena (2 papers), Science Education and Pedagogy (2 papers) and Particle physics theoretical and experimental studies (2 papers). The work is most often cited by research in Computer Science Applications (166 citations), Nuclear and High Energy Physics (204 citations), Statistical and Nonlinear Physics (173 citations), Astronomy and Astrophysics (97 citations) and Education (99 citations). Saif Rayyan has collaborated with scholars based in United States, Chile and South Korea. Frequent co-authors include Tatsu Takeuchi, Naotoshi Okamura, Djordje Minić, Lay Nam Chang, Sándor Benczik, David E. Pritchard, Daniel Seaton, Yoav Bergner, Gerd Kortemeyer and Isabel Hilliger. Their work appears in journals such as Journal of Computer Assisted Learning, Applied Optics, Journal of Computing in Higher Education, American Journal of Physics and AIP conference proceedings.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.